Learning Objectives

  • Understand basic grammar of plots in ggplot.
  • Produce scatter plots, and boxplots plots using ggplot.
  • Describe what faceting is and apply faceting in ggplot.
  • Learn to generate high quality plots using ggplot.


We start by loading the required packages. ggplot2 is included in the tidyverse package.

library(tidyverse)

Let’s read in our tomato yield data for learning various plots in R. We will use the data manipulation techniques, that we have learned before, and use it for visualization as well.

Plotting with ggplot2

ggplot2 simplifies the creation of intricate plots from data within a data frame. It offers a programmatic approach for specifying variables, their display, and overall visual properties. With this, minimal adjustments are needed if the data change or if switching between plot types. This streamlines the creation of publication-quality plots with minimal tweaking.

Graphics in ggplot are constructed by adding layers incrementally, providing extensive flexibility and customization options.

For building a ggplot, we’ll utilize a basic template adaptable to various plot types.

ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +  <GEOM_FUNCTION>()
  • use the ggplot() function and bind the plot to a specific data frame using the data argument
ggplot(data = yield_with_meata_df)

  • define a mapping (using the aesthetic (aes) function), by selecting the variables to be plotted and specifying how to present them in the graph, e.g. as x/y positions or characteristics such as size, shape, color, etc.
ggplot(data = yield_with_meata_df, mapping = aes(x = marketable_yield_kg, y = spad_value))

  • add ‘geoms’ – graphical representations of the data in the plot (points, lines, bars). ggplot2 offers many different geoms; we will use some common ones today, including:

    * `geom_point()` for scatter plots, dot plots, etc.
* `geom_boxplot()` for, well, boxplots!
* `geom_line()` for trend lines, time series, etc.

To add a geom to the plot use the + operator. Because we have two continuous variables, let’s use geom_point() first:

ggplot(data = yield_with_meata_df, mapping = aes(x = marketable_yield_kg, y = spad_value)) +
  geom_point()

The + in the ggplot2 package is particularly useful because it allows you to modify existing ggplot objects. This means you can easily set up plot templates and conveniently explore different types of plots, so the above plot can also be generated with code like this:

# Assign plot to a variable
gg_spad_yield <- ggplot(data = yield_with_meata_df, mapping = aes(x = marketable_yield_kg, y = spad_value))

# Draw the plot
gg_spad_yield + 
    geom_point()

Notes

  • Anything you put in the ggplot() function can be seen by any geom layers that you add (i.e., these are universal plot settings). This includes the x- and y-axis mapping you set up in aes().
  • You can also specify mappings for a given geom independently of the mappings defined globally in the ggplot() function.
  • The + sign used to add new layers must be placed at the end of the line containing the previous layer. If, instead, the + sign is added at the beginning of the line containing the new layer, ggplot2 will not add the new layer and will return an error message.
# This is the correct syntax for adding layers
gg_spad_yield +
  geom_point()
# This will not add the new layer and will return an error message
# gg_spad_yield
#   + geom_point()

Building your plots iteratively

Building plots with ggplot2 is typically an iterative process. We start by defining the dataset we’ll use, lay out the axes, and choose a geom:

ggplot(data = yield_with_meata_df, mapping = aes(x = marketable_yield_kg, y = spad_value))+
    geom_point()

Then, we start modifying this plot to extract more information from it. For instance, we can add transparency (alpha) to avoid overplotting:

ggplot(data = yield_with_meata_df, mapping = aes(x = marketable_yield_kg, y = spad_value))+
    geom_point(alpha = 0.1)

We can also add colors for all the points:

ggplot(data = yield_with_meata_df, mapping = aes(x = marketable_yield_kg, y = spad_value))+
    geom_point(alpha = 0.5, color = "blue")

Or to color each rootstock in the plot differently, you could use a vector as an input to the argument color. ggplot2 will provide a different color corresponding to different values in the vector. Here is an example where we color with rootstock:

ggplot(data = yield_with_meata_df, mapping = aes(x = marketable_yield_kg, y = spad_value))+
    geom_point(alpha = 0.5, aes(color = rootstock))

We can also specify the colors directly inside the mapping provided in the ggplot() function. This will be seen by all geom layers and the mapping will be determined by the x- and y-axis set up in aes().

ggplot(data = yield_with_meata_df, mapping = aes(x = marketable_yield_kg, y = spad_value, color = rootstock))+
    geom_point(alpha = 0.5)

Notice that we can change the geom layer and colors will be still determined by rootstock

Challenge

Use ggplot() to create a scatter plot of marketable_yield_kg and rootstock with marketable_yield_kg on the Y-axis, and rootstock on the X-axis. Have the colors be coded by rootstock. Is this a good way to show this type of data? What might be a better graph?

ANSWER 
ggplot(data = yield_with_meata_df, mapping = aes(x = rootstock, y = marketable_yield_kg, color = rootstock))+
    geom_point(alpha = 0.5)


Boxplot

We can use boxplots to visualize the distribution of weight within each rootstock:

ggplot(data = yield_with_meata_df, mapping = aes(x = rootstock, y = marketable_yield_kg, color = rootstock))+
    geom_boxplot()

By adding points to boxplot, we can have a better idea of the number of measurements and of their distribution.

Let’s also use the geometry “jitter”. geom_jitter is almost like geom_point but it allows you to visualize how the density of points because it adds a small amount of random variation to the location of each point.

ggplot(data = yield_with_meata_df, mapping = aes(x = rootstock, y = marketable_yield_kg))+
    geom_boxplot(alpha = 0) +
    geom_jitter(alpha = 0.5, color = "tomato") #notice our color needs to be in quotations

Notice how the boxplot layer is behind the jitter layer? What do you need to change in the code to put the boxplot in front of the points such that it’s not hidden?

Challenges

  1. Boxplots are useful summaries, but hide the shape of the distribution. For example, if the distribution is bimodal, we would not see it in a boxplot. An alternative to the boxplot is the violin plot, where the shape (of the density of points) is drawn.
  • Replace the box plot code from above with a violin plot; see geom_violin().
  1. In many types of data, it is important to consider the scale of the observations. For example, it may be worth changing the scale of the axis to better distribute the observations in the space of the plot. Changing the scale of the axes is done similarly to adding/modifying other components (i.e., by incrementally adding commands). Try making these modifications:
  • Use the violin plot you made in Q1 and adjust the weight to be on the log10 scale; see scale_y_log10().
  1. Make a new plot to explore the distribution of marketable_yield_kg just for rootstocks Nongraft and Maxifort using geom_boxplot(). Overlay a jitter/scatter plot of the marketable_yield_kg of the two rootstocks behind the boxplots. Then, add an aes() argument to color the datapoints (but not the boxplots) according to the plantID from which the sample was taken.
ANSWER 
#1 

ggplot(data = yield_with_meata_df, mapping = aes(x = rootstock, y = marketable_yield_kg))+
    geom_jitter(alpha = 0.5, color = "tomato") +
    geom_violin(alpha = 0)

# 2
ggplot(data = yield_with_meata_df, mapping = aes(x = rootstock, y = marketable_yield_kg))+
    geom_jitter(alpha = 0.5, color = "tomato") +
    geom_violin(alpha = 0) + 
    scale_y_log10()

#3

yield_with_meata_df %>% 
  filter(rootstock=="Nongraft" | rootstock=="Maxifort") %>%
  ggplot(mapping = aes(x = rootstock, y = marketable_yield_kg))+
    geom_jitter(alpha = 0.3, aes(color = plantID)) +
   geom_boxplot()


Faceting

ggplot2 has a special technique called faceting that allows the user to split one plot into multiple plots based on a factor included in the dataset. We will use it to make a time series plot for each rootstock:

yield_with_meata_df %>% 
  ggplot(data = yield_with_meata_df, mapping = aes(x = rootstock, y = marketable_yield_kg, color = rootstock)) +
    geom_jitter(alpha = 0.3) +
    geom_boxplot()+
  facet_wrap(~ sampling_date)

ggplot2 themes

ggplot Themes are a great, easy addition that can make all your plots more readable (and a lot more pretty!)

In addition to theme_bw(), which changes the plot background to white, ggplot2 comes with several other themes which can be useful to quickly change the look of your visualization. The complete list of themes is available at http://docs.ggplot2.org/current/ggtheme.html. theme_minimal() and theme_light() are popular, and theme_void() can be useful as a starting point to create a new hand-crafted theme.

Usually plots with white background look more readable when printed. We can set the background to white using the function theme_bw(). Additionally, you can remove the grid:

yield_with_meata_df %>% 
  ggplot(data = yield_with_meata_df, mapping = aes(x = rootstock, y = marketable_yield_kg, color = rootstock)) +
    geom_jitter(alpha = 0.3) +
    geom_boxplot()+
  facet_wrap(~ sampling_date)+
   theme_bw() +
     theme(panel.grid = element_blank())

Challenge 1

How to make high quality plot for the publication? We want to show the plot of marketable_yield_kg yield, by two sampling date. Here, we want to play with various factors of ggplot and themes.

ANSWER 
# change the date so that they are in the same foramt
yield_with_meta_date_fix_df <- yield_with_meata_df %>%
  mutate(date_column = ifelse(sampling_date=="july-15-2018", "07/15/2018","06/15/2018"))

##
yield_with_meta_date_fix_df %>%
  ggplot(mapping = aes(x = rootstock, y = marketable_yield_kg)) +
    geom_jitter(alpha = 0.5) +
    geom_boxplot()+
  facet_wrap(~ date_column)+
   theme_classic() +
     theme(panel.grid = element_blank())

###

## change the order of the rootstocsk. We want Nongraft, Selfgraft, RST-04-106, and Maxifort
yield_with_meta_date_fix_df$rootstock <- factor(yield_with_meta_date_fix_df$rootstock, levels = c("Nongraft", "Selfgraft","RST-04-106", "Maxifort"))

yield_with_meta_date_fix_df %>%
  ggplot(mapping = aes(x = rootstock, y = marketable_yield_kg)) +
    geom_jitter(alpha = 0.5) +
    geom_boxplot()+
  facet_wrap(~ date_column)+
   theme_classic() +
     theme(panel.grid = element_blank())

## Chnage the Y axis and X axis label

yield_with_meta_date_fix_df %>%
  ggplot(mapping = aes(x = rootstock, y = marketable_yield_kg)) +
    geom_jitter(alpha = 0.5) +
    geom_boxplot()+
  facet_wrap(~ date_column)+
  labs(x="Rootstocks", y="Marketable Yield (Kg)")+
   theme_classic() +
     theme(panel.grid = element_blank())

## line thinkness
yield_with_meta_date_fix_df %>%
  ggplot(mapping = aes(x = rootstock, y = marketable_yield_kg)) +
    geom_jitter(alpha = 0.5) +
    geom_boxplot()+
  facet_wrap(~ date_column)+
  labs(x="Rootstocks", y="Marketable Yield (Kg)")+
   theme_classic() +
     theme(panel.grid = element_blank(),
           axis.line = element_line(linewidth = 1))

## 
gg_marketable_yiled_by_rootstocks_sdate <- yield_with_meta_date_fix_df %>%
  ggplot(mapping = aes(x = rootstock, y = marketable_yield_kg)) +
    geom_jitter(alpha = 0.8) +
    geom_boxplot()+
  facet_wrap(~ date_column)+
  labs(x="Rootstocks", y="Marketable Yield (Kg)")+
   theme_classic() +
     theme(panel.grid = element_blank(),
           axis.line = element_line(linewidth = 1),
           axis.title = element_text(size = 20, face = "bold"),
           axis.text.x = element_text(size = 14, angle = 45, vjust = 1, hjust = 1),
           axis.text.y = element_text(size = 14))+
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        strip.background = element_blank(),
        panel.border = element_blank(),
        strip.text = element_text(size = 20))

gg_marketable_yiled_by_rootstocks_sdate

## save the plot in pdf
ggsave("output/figures/gg_marketable_yiled_by_rootstocks_sdate.pdf", gg_marketable_yiled_by_rootstocks_sdate, height = 5, width = 5 )


Challenge 2

Use what you just learned to create a plot that depicts how the average weight of each rootstock changes through the years.

ANSWER 
#create a new dataframe 
average_market_yiled_by_rootstocsk <- yield_with_meta_date_fix_df %>%
                 group_by(rootstock) %>%
                 summarize(avg_weight = mean(marketable_yield_kg))

ggplot(data = average_market_yiled_by_rootstocsk, mapping = aes(x=rootstock, y=avg_weight)) +
   geom_col() +
   labs(x="Rootstocks", y="Marketable Yield (Mean)", title = "this is a barplot")+
   theme_classic()